Problem 1
# Read in data
global_pps <- read.csv("../ppol563_psets/global_power_plant_database_v_1_3/global_power_plant_database.csv")
# Filter data down to power plants in the US
# Then, narrow data down using the maximum and minimum values for longitude and latitude for the DMV
global_pps <- global_pps %>%
filter(country=='USA') %>%
mutate(long = longitude, lat=latitude) %>%
filter(long>-83.64611 & long<(-75.05746)) %>%
filter(lat>36.53752 & lat<40.64563) %>%
select(lat, long, capacity_mw)
# Use the maps package to do reverse geocoding and get the state and county names for the location of each power plant in the dataset
startm <- Sys.time()
state<-map.where(database="state",
global_pps$long, global_pps$lat)
county<-map.where(database="county",
global_pps$long, global_pps$lat)
endm <- Sys.time()
# Further subset and clean the data using the information from the reverse geocoding
global_pps_subset <- cbind(global_pps, state)
global_pps_subset$state[global_pps_subset$state=='virginia:main'] <- 'virginia'
global_pps_subset$state[global_pps_subset$state=='virginia:chesapeake'] <- 'virginia'
global_pps_subset <- global_pps_subset %>%
filter(state %in% c("maryland", "virginia", "west virginia", "district of columbia"))
# Get county map for DMV
dmv_county_map <- map_data(map = "county", region = c("virginia", "maryland", "west virginia", "district of columbia"))
dmv_state_map <- map_data(map = "state", region = c("virginia", "maryland", "west virginia", "district of columbia"))
# Load data on US cities
data("us.cities")
# Plot the data
dmv_map <- ggplot() +
geom_polygon(data=dmv_county_map,
aes(long, lat, group = group),
fill="white",
color="lightgray") +
coord_map("polyconic") +
geom_path(data=dmv_state_map, aes(long, lat, group=group), colour ="black") +
geom_point(data=global_pps_subset, aes(x=long, y=lat, size=capacity_mw, color=state, alpha=0.5)) +
geom_text_repel(data=us.cities %>% filter(country.etc %in% c("DC","VA","MD","WV")), aes(x=long, y=lat, label=name), size=2, max.overlaps=15) +
labs(title="Power Plants in the Greater Washington Area by Capacity", subtitle="Average capacity of power plants highest in WV", x=NULL, y=NULL) +
theme_void()
dmv_map
## Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Problem 2
# Use census API key
census_api_key("8d0cd9673a66af24790039fc11e19c97e9b85d99", install = TRUE, overwrite=TRUE)
## Your original .Renviron will be backed up and stored in your R HOME directory if needed.
## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
## To use now, restart R or run `readRenviron("~/.Renviron")`
## [1] "8d0cd9673a66af24790039fc11e19c97e9b85d99"
# Load census data
acs_2020 <- load_variables(year = 2020, "acs5",cache = TRUE)
# Get the median income variable
census_data_MI <- get_acs(geography="county",
state="VA",
year=2020,
variables="B19013_001")
## Getting data from the 2016-2020 5-year ACS
# Select and clean data
census_data_MI <- census_data_MI %>%
mutate(median_income=estimate) %>%
select(GEOID, NAME, median_income)
# Get the population data
census_data_pop <- get_acs(geography="county",
state="VA",
year=2020,
variables="B01003_001")
## Getting data from the 2016-2020 5-year ACS
# Select and clean data
census_data_pop <- census_data_pop %>%
mutate(population=estimate) %>%
select(GEOID, NAME, population)
# Merge median income and population data
census_data <- left_join(census_data_MI, census_data_pop)
## Joining, by = c("GEOID", "NAME")
# Create "interaction" variable to calculate median income for nova area
census_data$mi_pop <- census_data$median_income*census_data$population
# Create list of nova counties and independent cities to aggregate
nova_counties <- c("Arlington County, Virginia",
"Fairfax County, Virginia",
"Loudoun County, Virginia",
"Prince William County, Virginia",
"Stafford County, Virginia",
"Alexandria city, Virginia",
"Fairfax city, Virginia",
"Falls Church city, Virginia",
"Manassas city, Virginia",
"Manassas Park city, Virginia",
"Fredericksburg city, Virginia",
"Clarke County, Virginia",
"Culpeper County, Virginia",
"Fauquier County, Virginia",
"Madison County, Virginia",
"Rappahannock County, Virginia",
"Spotsylvania County, Virginia",
"Stafford County, Virginia",
"Warren County, Virginia",
"Winchester city, Virginia")
# Get the total nova population
nova_pop <- census_data %>%
filter(NAME %in% nova_counties) %>%
select(population) %>%
sum()
# Sum the median income of nova counties and then divide it by total nova population
nova_mi <- census_data %>%
filter(NAME %in% nova_counties) %>%
select(mi_pop) %>%
sum()/nova_pop
# Create nova dataframe and merge it with the existing census data
GEOID <- ""
NAME <- "NoVA"
median_income <- nova_mi
population <- nova_pop
mi_pop <- nova_mi*nova_pop
nova_df <- data.frame(GEOID, NAME, median_income, population, mi_pop)
census_data <- rbind(census_data, nova_df)
# Get rid of "county, virginia" from county names so cen
census_data$NAME=gsub(" County, Virginia","",census_data$NAME)
census_data$NAME=tolower(census_data$NAME)
colnames(census_data)[2] <- "subregion"
# Get virginia county data and label nova counties as nova
va_map <- map_data(map="county", region="virginia")
va_map["subregion"][va_map["subregion"]=="arlington"] <- "nova"
va_map["subregion"][va_map["subregion"]=="fairfax"] <- "nova"
va_map["subregion"][va_map["subregion"]=="loudoun"] <- "nova"
va_map["subregion"][va_map["subregion"]=="prince william"] <- "nova"
va_map["subregion"][va_map["subregion"]=="stafford"] <- "nova"
va_map["subregion"][va_map["subregion"]=="frederick"] <- "nova"
va_map["subregion"][va_map["subregion"]=="clarke"] <- "nova"
va_map["subregion"][va_map["subregion"]=="fauquier"] <- "nova"
va_map["subregion"][va_map["subregion"]=="madison"] <- "nova"
va_map["subregion"][va_map["subregion"]=="culpeper"] <- "nova"
va_map["subregion"][va_map["subregion"]=="rappahannock"] <- "nova"
va_map["subregion"][va_map["subregion"]=="spotsylvania"] <- "nova"
va_map["subregion"][va_map["subregion"]=="warren"] <- "nova"
# Merge census data with virginia county geographic data
va_map <- left_join(va_map, census_data)
## Joining, by = "subregion"
# Plot data
va_mi_plot <- ggplot(va_map, aes(x=long, y=lat, group=group)) +
geom_polygon(aes(fill=median_income)) +
coord_map("mercator") +
scale_fill_viridis(option = "B", labels=~paste("$", .x/1000, "K"), name="Median Income") +
geom_text(x=-77.8, y=38.75, label="NoVA")+
labs(title="Median Household Income in Virginia by County", subtitle="Median income is highest in the Northern Virginia suburbs of Washington", x=NULL, y=NULL) +
theme_void()
va_mi_plot
Problem 3
# Create variable that measures margin of victory for the winner
election$trump_margin <- ifelse(election$winner=="Clinton",-1*(election$pct_margin),election$pct_margin)
# Change the variable name to prepare for merging
election <- election %>%
mutate(state = st)
# Join hexagon bin data with election data
hexagon_bins <- left_join(sf_NPR1to1, election, by="state")
## old-style crs object detected; please recreate object with a recent sf::st_crs()
# Plot data
ggplot(hexagon_bins) +
geom_sf(aes(fill=trump_margin, color=factor(winner))) +
geom_sf_text(aes(label = state), color = "white") +
labs(x=NULL, y=NULL, title="Republican Margin Percentages in 2016 Presidential Election", subtitle="Coastal states went democrat while midwestern and plains states went republican") +
scale_fill_gradient2(low="darkblue", mid="gray", high="darkred", name="Trump Margin", labels=~paste(.x, "%")) +
scale_color_manual(values=c("Blue","Red"), name="Winner") +
theme_void()
Problem 4
# Get stock data from tidyquant
stock_prices <- tq_get(c("META","AAPL","AMZN","NFLX","GOOG"), get="stock.prices")
# Design plot
p1 <- ggplot(stock_prices, aes(x=volume, y=adjusted)) +
geom_point(aes(color=symbol), size=12, alpha=0.7) +
scale_color_discrete(NULL, labels=c("Apple","Amazon","Google","Meta","Netflix")) +
scale_x_log10(labels=function(s) format(x=s/1000000, big.mark = ",")) +
scale_y_continuous(labels=~paste("$",.x)) +
labs(title="Adjusted Price and Volume for FAANG stocks: {frame_time}", x="Trading Volume (MM)", y="Adjusted Price") +
transition_time(date) +
ease_aes('linear') +
shadow_wake(0.2)
# Animate plot frames
animate(p1, nframes=300)
Problem 5
# Save gun background check data
guns <- read.csv("../ppol563_psets/gun_background_checks.csv")
# Read in a US state map and get a list of states
us_state_map <- map_data("state")
us_states <- unique(us_state_map$region)
# Get recheck data from July 2022
p5_data <- guns %>%
mutate(region = tolower(state)) %>%
filter(region %in% us_states & month=="7/1/2022") %>%
mutate(private_sale_prop = (private_sale_handgun+private_sale_long_gun+private_sale_other)/totals,
recheck_prop = permit_recheck/totals)
# Join state map with background check data
us_state_map <- left_join(us_state_map, p5_data, by="region")
# Plot data
recheck_choropleth <- ggplot(us_state_map, aes(x=long, y=lat, group=group)) +
geom_polygon(aes(fill=recheck_prop), color="black") +
coord_map("albers", lat0=29.5, lat1=45.4) +
scale_fill_viridis(option="F", direction=-1, labels=~paste(.x*100, "%"), name="Proportion of rechecks") +
labs(title="Proportion of permit rechecks to checks requested by a\n Federal Firearm Licensee, July 2022", subtitle="Kentucky, Utah, Indiana, and Minnesota have a high proportion of rechecks\n while most states are close to 0") +
theme_void()
recheck_choropleth
Problem 6
# Use stock price data from problem 4 to plot an animated line plot
faang_line_plot <-ggplot(stock_prices, aes(x=date, y=adjusted, color=symbol)) +
geom_line() +
geom_point() +
scale_color_discrete(NULL, labels=c("Apple","Amazon","Google","Meta","Netflix")) +
scale_y_continuous(labels=~paste("$",.x)) +
labs(title="Adjusted prices of FAANG Stocks, 2012-2022", subtitle="Meta and Netflix prices diverge from other FAANG prices in 2016 and crash in 2022", x="Year", y="Adjusted Price") +
transition_reveal(date) +
theme_minimal()
animate(faang_line_plot, nframes=150, end_pause=15)
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
